IEEE Transactions on Dependable and Secure Computing最新文献

筛选

英文中文

CSCAD: An Adaptive LightGBM Algorithm to Detect Cache Side-Channel Attacks CSCAD：检测高速缓存侧信道攻击的自适应 LightGBM 算法

IEEE Transactions on Dependable and Secure Computing Pub Date : 2024-07-01 DOI: 10.1109/tdsc.2024.3415376

Sirui Hao, Junjiang He, Wenshan Li, Tao Li, Geying Yang, Wenbo Fang, Wanying Chen

引用次数: 0

A Semantic, Syntactic, And Context-Aware Natural Language Adversarial Example Generator 语义、句法和上下文感知自然语言对抗示例生成器

IEEE Transactions on Dependable and Secure Computing Pub Date : 2024-03-18 DOI: 10.1109/TDSC.2024.3359817

Javad Asl, Mohammad H. Rafiei, Manar Alohaly, Daniel Takabi

{"title":"A Semantic, Syntactic, And Context-Aware Natural Language Adversarial Example Generator","authors":"Javad Asl, Mohammad H. Rafiei, Manar Alohaly, Daniel Takabi","doi":"10.1109/TDSC.2024.3359817","DOIUrl":"https://doi.org/10.1109/TDSC.2024.3359817","url":null,"abstract":"Machine learning models are vulnerable to maliciously crafted Adversarial Examples (AEs). Training a machine learning model with AEs improves its robustness and stability against adversarial attacks. It is essential to develop models that produce high-quality AEs. Developing such models has been much slower in natural language processing (NLP) than in areas such as computer vision. This paper introduces a practical and efficient adversarial attack model called SSCAE for textbf{S}emantic, textbf{S}yntactic, and textbf{C}ontext-aware natural language textbf{AE}s generator. SSCAE identifies important words and uses a masked language model to generate an early set of substitutions. Next, two well-known language models are employed to evaluate the initial set in terms of semantic and syntactic characteristics. We introduce (1) a dynamic threshold to capture more efficient perturbations and (2) a local greedy search to generate high-quality AEs. As a black-box method, SSCAE generates humanly imperceptible and context-aware AEs that preserve semantic consistency and the source language's syntactical and grammatical requirements. The effectiveness and superiority of the proposed SSCAE model are illustrated with fifteen comparative experiments and extensive sensitivity analysis for parameter optimization. SSCAE outperforms the existing models in all experiments while maintaining a higher semantic consistency with a lower query number and a comparable perturbation rate.","PeriodicalId":508198,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":"352 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0